The construction industry is currently facing a massive labor crisis, with nearly half a million positions unfilled in the U.S. alone. To bridge this gap, Bedrock Robotics is developing autonomous systems that allow heavy machinery to operate with minimal human oversight. However, training these "physical AI" systems requires labeling millions of hours of video footage to teach machines how to recognize specific tools and tasks. Traditionally, this was a manual, grueling bottleneck that hindered the scaling of autonomous fleets.

By partnering with the AWS Generative AI Innovation Center, Bedrock Robotics turned to vision-language models (VLMs) to automate this data preparation. These models act as bridge-builders, connecting visual data from excavator cabins with natural language descriptions. Because standard models often struggle with the dust, odd angles, and specialized tools of a worksite, the team utilized advanced prompt engineering to provide the AI with domain-specific context. This refined approach effectively taught the models to distinguish between similar equipment, such as grading beams and trenching buckets.

The results are transformative for industrial automation. The company saw a jump in tool identification accuracy from 34% to 70%, all while maintaining a cost of just $10 per hour of processed video. This transition from manual labeling to a scalable, VLM-powered pipeline allows for faster training cycles and more resilient autonomous equipment. As labor shortages persist, this framework offers a repeatable blueprint for other physical AI sectors like logistics and manufacturing to accelerate their deployment of intelligent, real-world machines.

The construction industry is currently facing a massive labor crisis, with nearly half a million positions unfilled in the U.S. alone. To bridge this gap, Bedrock Robotics is developing autonomous systems that allow heavy machinery to operate with minimal human oversight. However, training these "physical AI" systems requires labeling millions of hours of video footage to teach machines how to recognize specific tools and tasks. Traditionally, this was a manual, grueling bottleneck that hindered the scaling of autonomous fleets.

By partnering with the AWS Generative AI Innovation Center, Bedrock Robotics turned to vision-language models (VLMs) to automate this data preparation. These models act as bridge-builders, connecting visual data from excavator cabins with natural language descriptions. Because standard models often struggle with the dust, odd angles, and specialized tools of a worksite, the team utilized advanced prompt engineering to provide the AI with domain-specific context. This refined approach effectively taught the models to distinguish between similar equipment, such as grading beams and trenching buckets.

The results are transformative for industrial automation. The company saw a jump in tool identification accuracy from 34% to 70%, all while maintaining a cost of just $10 per hour of processed video. This transition from manual labeling to a scalable, VLM-powered pipeline allows for faster training cycles and more resilient autonomous equipment. As labor shortages persist, this framework offers a repeatable blueprint for other physical AI sectors like logistics and manufacturing to accelerate their deployment of intelligent, real-world machines.